cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
 3   4   5   6   7 
Search results for , issue "Vol 35, No 2: August 2024" : 66 Documents clear
Developing Bluetooth phonocardiogram for detecting heart murmurs using hybrid MFCC and LSTM Wahyu Nugroho, Dwi Oktavianto; Hikmah, Nada Fitrieyatul; A’alimah, Fathin Hanum; Oktavia, Nabila Shafa; Dwi Winarsih, Meitha Auliana; Elparani, Sirsta Hayatu; Rifqi Hananto, R. M. Tejo
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp878-887

Abstract

Cardiovascular disease is a leading global cause of mortality. Most stethoscopes still necessitate the use of tubing, which entails direct physical contact between the healthcare provider and patient. The stethoscope can serve as a means of transmission if it is utilized on individuals who have been diagnosed with airborne and droplet-borne infectious illnesses. A prototype was created to capture heart sounds using a Phonocardiography (PCG) device over website-based Bluetooth connectivity. This approach offers the benefits of being cost-effective, facilitating computer-aided diagnostics, and being wearable. In addition, the primary significance of this study resides in the identification of heart sound irregularities caused by cardio dynamic abnormalities of the heart valves, known as murmurs. The heart sound categorization process utilizes a machine learning model that involves extracting 25 Mel frequency cepstral coefficients (MFCC) as features. The model employs a hybrid approach combining convolutional neural network and long short-term memory (CNN-LSTM) techniques. The research findings indicate that the suggested model achieves an average accuracy rate of 95.9% over five distinct categories, i.e., normal, atrial stenosis, mitral regurgitation, mitral stenosis, and mitral valves prolapse. Further study can be conducted on hardware development by incorporating an infrared sensor at the fingertip of the stethoscope.
PV system applied on the new PUC-NPC inverter Amari, Abdelhay Sadki; El Gadari, Ayoub; Arbaoui, Abdezzahid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp720-728

Abstract

In this article, the authors present a new competitive Packed-U-cell and neutral point clamped (PUC-NPC) 9-level converter that utilizes a small number of power switches and passive components. Only seven switches are added to a single DC source. However, the proposed converter takes benefits of the neutral point clamped (NPC) converter’s capability to connect capacitors in series, all with a simple control that enables the algorithm associated to the proposed inverter operate correctly in an open loop operation. A technique of pulse width modulation (PWM) is employed to maintain capacitor voltage at desired values. The proposed inverter produces an almost sinusoidal waveform output voltage, as result, a perfectly sinusoidal charge current with minimal impact on the economy and energy consumption are optioned. The converter has been integrated with a photovoltaic system to minimize its impact on the electricity supply and enhance energy efficiency. In order to take a maximum power from the photovoltaic panel, authors employ a maximum power point tracking (MPPT) technique based on disturbance and observe (P and O). When combined with the proposed control technique, it offers a competitive system suitable for standalone use or as an energy source, eliminating the need for PI regulators or additional investments. The proposed 9-level converter has been validated through simulation, utilizing the MATLAB Simulink environment to model the proposed system. Dynamics of this later were verified by changing the load charge.
A relational background knowledge boosting based topic model for Chinese poems Peng, Lei; Porntrakoon, Paitoon
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1227-1243

Abstract

Classical Chinese poetry has been increasingly popular in recent years, and modeling its topic is quite a promising area of research. Chinese poems have the characteristic of short in length, but traditional topic models perform poorly when faced with short texts due to the text sparsity. Therefore, topic model should be improved to satisfy the scenario of classical Chinese poems. In this paper, a relational background knowledge boosting based topic model (RBKBTM) was proposed to overcome the text sparsity of Chinese poems. We incorporated background information into the model, which expanded the text content from the semantic perspective. The background knowledge was combined using word embedding and TextRank and was then fed into the core computing process. Subsequently, a new sampling formula was derived. Our proposed model was tested on three different tasks using three different datasets. The results demonstrate that the incorporated background knowledge can effectively overcomes text sparsity, improving the performance and effectiveness of the topic model.
A new highly efficient MAC protocol for WBAN: exceptional performance in the face of selfish behaviors Azdad, Nabila; Elboukhari, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1082-1090

Abstract

Over the last two decades, wireless body area networks (WBANs) have gained significant traction in healthcare applications. These networks facilitate connections among various sensors, which can be integrated into clothing, placed directly on the body, or implanted beneath the skin. While these sensors typically serve a single application, they generate traffic with diverse requirements. Managing this diversity necessitates tailored treatment to meet specific traffic needs while satisfying application requirements such as reliability and timeliness. In this paper, we propose a novel, flexible, and power-efficient medium access control (MAC) protocol designed to seamlessly complement existing solutions. Our protocol, available in two versions as an enhancement to the beacon-enabled mode of IEEE 802.15.4, aims to optimize quality of service (QoS) for periodic traffic applications within WBANs, irrespective of traffic and density conditions, without compromising energy efficiency. Our results demonstrate significant improvements compared to the standardized IEEE 802.15.4-MAC protocol across all test scenarios, even in the presence of selfish behaviors. These findings underscore the protocol’s efficacy in enhancing reliability and efficiency in wireless healthcare systems.
Optimizing wireless sensor networks using centrality metrics: a strategic approach Kallakunta, Suneela; Sreenivas, Alluri
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1181-1190

Abstract

This research paper presents a methodology for improving wireless sensor network (WSN) performance by leveraging centrality measures, including degree, betweenness, closeness, eigenvector, and Katz centrality. Employing a random walk graph model, this study constructs networks with 30 and 50 nodes to investigate the impact of these centrality metrics on routing decisions to optimize energy efficiency, minimize latency, and enhance overall network reliability. Additionally, the paper provides a comprehensive analysis of the relationships among these centrality measures through various correlation techniques, such as Pearson correlation, Kendall rank correlation, and Spearman correlation, offering insights into how these metrics can effectively improve WSN operations.
Gesture recognition technology: a new dimension in human-computer interaction interface Beisov, Nurbol; Madyarova, Gulnar; Kerimbayev, Nurassyl
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1311-1324

Abstract

Development of an interface for intelligent gesture control to improve user experience and increase the efficiency of interaction with a computer. This paper proposes a gesture recognition system based on artificial intelligence using convolutional neural networks (CNN). The system comprises three stages: pre-processing, optimal frame determination, and gesture category identification. The extracted features used are independent of movement, scaling, and rotation, providing greater flexibility to the system. The suggested gesture control technology, known as Kazakh Sign Language (KSL) for Kazakh alphabets, eliminates the need for additional devices, enabling users to interact with the system naturally. Experiments demonstrated that the proposed KSL system can accurately recognize Kazakh language alphabet letters with a high precision of 97.3%, owing to the utilization of artificial intelligence and CNN to enhance the accuracy and effectiveness of gesture control. Gestures, a type of visual formation, are perceivable by computers through machine learning models. The selection of methods and systems for recognizing Kazakh sign language gestures was accompanied by addressing various challenges related to language-specific orthographic and gestural features. The developed gesture control interface for human-computer interaction is applied in the field of inclusive education, aiming to assist deaf and hard-of-hearing children in learning sign language.
Advanced control of double stage grid-tied three phase photovoltaic systems with shunt active power filter Zaghar, Fatim-Zahra; Hekss, Zineb; Rafi, Mohamed; Ridah, Abderraouf; Adhiri, R’hma
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp673-682

Abstract

This paper explores the challenges associated with the control of a two-stage three-phase electrical grid connected to a photovoltaic (PV) system. The objectives encompass: i) maximizing the available PV power, ii) controlling the DC-link voltage to a predetermined setpoint, and iii) considering that power quality has become an important measure in a distribution electrical network where different loads are connected, the third objective will mainly focus on ensuring power factor correction (PFC). To achieve these objectives, two loops of nonlinear controller are developed. In the outer loop, the duty cycle of a boost converter is controlled using a hybrid technique of backstepping technique and the perturb & observe (P&O) algorithm. In addition, the inner loop employs a hybrid automaton approach to tackle the challenges of a three-phase shunt active power filter (SAPF). The results have been verified through numerical simulation using MATLAB/Simulink power systems environment.
Processes monitoring using adaptive confidence limit based on T-S fuzzy model and Luenberger observer Bouzenad, Khaled; Rahmouni, Salah; Ramdani, Messaoud
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp844-852

Abstract

In hazard-sensitive processes, the monitoring upsets and malfunctions correctly is an important challenge to operation safely and enhance the performance. Conventional process monitoring frequently assumes that process data follow only one Gaussian distribution, which generates a constant confidence limit and hence produces a high number of false alarms. However, in fact, industrial processes usually include various operating modes. To ovoid this drawback, the suggested approach employs an adaptive confidence limit (ACL) when a substantial number of false alarms are created. The fundamental concept underlying this study is to extract internally several local linear sub-modes of the monitored variables. In typical operating circumstances, the Gaussian mixture model (GMM) is utilized to extract several local linear sub-modes, followed by fuzzy linearization using the Takagi-Sugeno model, thereafter a bank of Luenberger observers to construct the residual spaces. An abnormal event is detected when the squared prediction error (SPE) is too great or exceeds the adaptive threshold designed to prevent the false alarms. Furthermore, an enhanced contribution plots is effectively used to identify the defective variable.
Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning Gulame, Mayuresh B.; Dixit, Vaibhav V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp996-1003

Abstract

Thyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes.
Text document clustering using mayfly optimization algorithm with k-means technique Dodda, Ratnam; Babu, Alladi Suresh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1099-1109

Abstract

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).

Page 6 of 7 | Total Record : 66

 3   4   5   6   7 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue